1. Technical Field
A “Wearable Electromyography-Based Controller” provides a physical device, worn by or otherwise attached to a user, that directly senses and decodes electrical signals produced by human muscular activity using surface Electromyography (sEMG) sensors. The resulting electrical signals provide a muscle-computer interface for use in controlling or interacting with one or more computing devices or other devices coupled to a computing device.
2. Related Art
In general, as is well known to those skilled in the art, Electromyography (EMG) measures the muscle electrical activity during muscle contractions as an electrical potential between a ground electrode and a sensor electrode. EMG can measure signals either directly within the muscle (invasive EMG) or on the skin above a muscle (surface EMG).
Invasive EMG is very accurate in sensing muscle activation, but is generally considered to be impractical for human-computer interaction applications as it requires needle electrodes to be inserted through the skin and directly into the muscle fibers. In contrast, surface EMG, while less accurate, only requires that conductive sensors be placed on the surface of the skin. Surface EMG is fundamentally noisier than invasive EMG since motor unit action potentials (MUAPs) must pass though body tissues such as fat and skin before they can be captured by a sensor on the surface. Due to the high sensitivity of EMG sensors required to detect these signals, they also typically detect other electrical phenomena such as activity from other muscles, skin movement over muscles, and environmental noise, etc.
The EMG signal is an electrical potential, or voltage, changing over time. The raw signal is an oscillating wave with an amplitude increase during muscle activation. Most of the power of this signal is contained in the frequency range of 5 to 250 Hz. A typical statistic computed over the raw EMG signal for diagnosis of muscle activity is the windowed root mean squared (RMS) amplitude of the measured potential. This RMS measure of EMG signals has typically been employed for diagnostic purposes such as evaluating muscle function during rehabilitation after a surgery or for measuring muscle activation to assess gait. RMS amplitude is a rough metric for how active a muscle is at a given point in time. Consequently, since most EMG-based applications have originated and are used in medical and/or clinical settings, certain assumptions are generally made about preparation and setup of EMG measurement devices, and about the measurement and processing of EMG signals.
For example, since the medical utility of attaining the best possible signal is high, there is typically no perceived need to reduce the cost of preparation and setup at the cost of signal accuracy. Specifically, in setting up EMG devices in a clinical setting, the skin is typically first cleaned with an abrasive so that dead skin cells are removed. EMG sensors are then typically carefully placed by an expert, who can locate the exact locations of muscle bellies and find the optimal placement. Further, in some cases, a current is then applied through the sensors to test sensor placement accuracy. For example, if the electrode is placed on a muscle that is expected to control a particular finger and a current is applied to the muscle via the electrode in the EMG sensor, the expected finger should twitch, if not, then sensor would be relocated to the correct position.
Further, since EMG sensors are generally carefully placed in clinical settings, they are usually treated as being static (i.e., mapped directly to specific muscles of interest). Consequently, clinicians tend to place many constraints on users/patients in these scenarios. For example, the users/patients may not be allowed to move their bodies in certain ways (e.g., rotating the arm, since this would move the surface sensors away from the muscles of interest).
Human-computer interfaces (HCl) have been primarily implemented by monitoring direct manipulation of devices such as mice, keyboards, pens, dials, touch sensitive surfaces, etc. However, as computing and digital information becomes integrated into everyday environments, situations arise where it may be inconvenient or difficult to use hands to directly manipulate an input device. For example, a driver attempting to query a vehicle navigation system might find it helpful to be able to do so without removing his or her hands from the steering wheel. Further, a person in a meeting may wish to unobtrusively and perhaps invisibly interact with a computing device. Unfortunately, the general assumptions described above with respect to the setup and use of conventional EMG sensors and signal measurement tend to make the use and setup of conventional EMG systems impractical for typical HCl purposes which allow a user to control and interact with computing systems, applications, and attached devices.